Time-dependent ROC curve is commonly used for evaluating the medical practice performance (or decision making) for detecting a time-to-event outcome. In this study, we introduce a new semi-parametric regression method for estimating time-dependent ROC curve with longitudinal biomarker measurements, which can adjust for covariates, based on a transformation time-to-event model. Since the transformation model does not place any assumptions on the distribution of an event time outcome, this approach can be applied to more general case and is more robust than previous semi-parametric methods. Numerical study was implemented for the heteroscedastic transformation model when the error term follows extreme value distribution, standard normal distribution and logistic distribution. The results show that our estimator is unbiased and robust to mis-specification of the time-to-event model. The efficiency is comparable with the correctly specified model and much higher than the mis-specified model. The new method was applied to analyze data from HIVNET 012 randomized trial for evaluating the two biomarkers of predicting mother-to-infant transmission of HIV-1 virus.